OLAP databases: what's new and what's best in 2026

In 2025, choosing an OLAP (Online Analytical Processing) database involves navigating a landscape significantly evolved with features like vector search, lakehouse formats, and serverless ingestion becoming standard. OLAP databases, designed for complex queries across large datasets, differ from traditional transactional databases by using columnar storage, which significantly enhances performance for analytical queries. The year's innovations include the integration of vector similarity search, adoption of open table formats like Apache Iceberg for better interoperability, and the default separation of storage and compute to improve scalability and cost-efficiency. The text compares leading open source and managed OLAP databases, such as ClickHouse®, Apache Druid, Apache Pinot, and StarRocks, highlighting their unique features and use cases. Managed services like ClickHouse® Cloud and Tinybird simplify the operational complexities of running OLAP databases in production, allowing developers to focus more on data pipelines than infrastructure management. The choice of the best OLAP database depends on various factors, including data volume, query latency, ingestion patterns, SQL dialect compatibility, and total cost of ownership, with considerations for both managed and self-hosted deployment options.